R Notebook

require(pRoloc)

Loading required package: pRoloc
Loading required package: MLInterfaces
Loading required package: annotate
Loading required package: AnnotationDbi
Loading required package: IRanges

Attaching package: 㤼㸱IRanges㤼㸲

The following object is masked from 㤼㸱package:grDevices㤼㸲:

    windows

Loading required package: XML

Attaching package: 㤼㸱annotate㤼㸲

The following object is masked from 㤼㸱package:mzR㤼㸲:

    nChrom

Loading required package: cluster
Loading required package: BiocParallel
Registered S3 methods overwritten by 'dbplyr':
  method         from
  print.tbl_lazy     
  print.tbl_sql      
Registered S3 method overwritten by 'data.table':
  method           from
  print.data.table     

This is pRoloc version 1.28.0 
  Visit https://lgatto.github.io/pRoloc/ to get started.

require(pRolocdata)

Loading required package: pRolocdata

This is pRolocdata version 1.26.0.
Use 'pRolocdata()' to list available data sets.

setStockcol(paste0(getStockcol(), 90))
myBlues <- colorRampPalette(RColorBrewer::brewer.pal(9,"Blues"))(100)
set.seed(1)

#intial visualisation

fData(arabidposis_pbsrep1)$markers[fData(arabidposis_pbsrep1)$markers %in% c("cytosolic ribosomes", "extracellular")] <- "unknown"
plot2D(arabidposis_pbsrep1, grid = FALSE)
addLegend(arabidposis_pbsrep1, where = "topleft", cex = 0.7, ncol = 2)

lapply(out, plot)

[[1]]
NULL

[[2]]
NULL

[[3]]
NULL

[[4]]
NULL

checking convergence

require(coda)
araprbsrep1diag <- gelman.diag(out[c(2,3,4)])
araprbsrep1diag

Potential scale reduction factors:

     Point est. Upper C.I.
[1,]      0.999          1

arab_psb_rep1_params_conv <- arab_psb_rep1_params[c(2,3,4)]

arab_psb_rep1_params_conv <- tagmMcmcProcess(arab_psb_rep1_params_conv)
arabidposis_pbsrep1 <- tagmPredict(object = arabidposis_pbsrep1, params = arab_psb_rep1_params_conv)

save(arab_psb_rep1_params_conv, file = "arab_psb_rep1_params_conv.rda")
save(arabidposis_pbsrep, file = "arabidposis_pbsrep_tagm.rda")

Error in save(arabidposis_pbsrep, file = "arabidposis_pbsrep_tagm.rda") : 
  object ‘arabidposis_pbsrep’ not found

par(mfrow = c(1, 2))
plot2D(arabidposis_pbsrep1, fcol = "tagm.mcmc.allocation",
        cex = fData(arabidposis_pbsrep1)$tagm.mcmc.probability,
        main = "TAGM MCMC allocations")
addLegend(arabidposis_pbsrep1, fcol = "markers",
           where = "topleft", ncol = 2, cex = 0.6)

plot2D(arabidposis_pbsrep1, fcol = "tagm.mcmc.allocation",
        cex = fData(arabidposis_pbsrep1)$tagm.mcmc.mean.shannon,
        main = "Visualising global uncertainty")
addLegend(arabidposis_pbsrep1, fcol = "markers",
           where = "topleft", ncol = 2, cex = 0.6)

load("C:/Users/OllyC/Desktop/2019-LOPIT-Projects/2020-Harriet-Arabidopsis-/RawData/arabidposis_pbsrep1.rda")
arab_psb_rep1__markers2_params <- readRDS(file = "C:/Users/OllyC/Desktop/2019-LOPIT-Projects/2020-Harriet-Arabidopsis-/RawData/mcmc_arabidposis_pbsrep1_markers2.rds")

#intial visualisation

fData(arabidposis_pbsrep1)$markers2[fData(arabidposis_pbsrep1)$markers2 %in% c("cytosolic ribosomes", "extracellular")] <- "unknown"
plot2D(arabidposis_pbsrep1, grid = FALSE, fcol = "markers2")
addLegend(arabidposis_pbsrep1, where = "topleft", cex = 0.7, ncol = 2, fcol = "markers2")

out <- mcmc_get_outliers(arab_psb_rep1__markers2_params)

nChains <- length(arab_psb_rep1__markers2_params)
nChains

[1] 4

for (i in seq_len(nChains))
    plot(out[[i]], main = paste("Chain", i), auto.layout = FALSE, col = i)

NA

gelman.diag(out, transform = FALSE)

Potential scale reduction factors:

     Point est. Upper C.I.
[1,]      0.999          1

arab_psb_rep1__markers2_params_pooled <- mcmc_pool_chains(arab_psb_rep1__markers2_params)
arab_psb_rep1__markers2_params_pooled

arab_psb_rep1__markers2_params_pooled <- tagmMcmcProcess(arab_psb_rep1__markers2_params_pooled)
arab_psb_rep1__markers2_params_pooled

Object of class "MCMCParams"
Method: TAGM.MCMC 
Number of chains: 1 
Summary available

arabidposis_pbsrep1 <- tagmPredict(object = arabidposis_pbsrep1,
                           params = arab_psb_rep1__markers2_params_pooled,
                           probJoint = TRUE,
                           fcol = "markers2")
head(fData(arabidposis_pbsrep1))

NA

par(mfrow = c(1, 2))
plot2D(arabidposis_pbsrep1, fcol = "tagm.mcmc.allocation",
        cex = fData(arabidposis_pbsrep1)$tagm.mcmc.probability,
        main = "TAGM MCMC allocations")
addLegend(arabidposis_pbsrep1, fcol = "markers",
           where = "topleft", ncol = 2, cex = 0.6)

plot2D(arabidposis_pbsrep1, fcol = "tagm.mcmc.allocation",
        cex = fData(arabidposis_pbsrep1)$tagm.mcmc.mean.shannon,
        main = "Visualising global uncertainty")
addLegend(arabidposis_pbsrep1, fcol = "markers",
           where = "topleft", ncol = 2, cex = 0.6)

save(arab_psb_rep1__markers2_params_pooled, file = "arab_psb_rep1__markers2_params_pooled.rda")

Warning messages:
1: In readChar(file, size, TRUE) : truncating string with embedded nuls
2: In readChar(file, size, TRUE) : truncating string with embedded nuls
3: In readChar(file, size, TRUE) : truncating string with embedded nuls
4: In readChar(file, size, TRUE) : truncating string with embedded nuls
5: In readChar(file, size, TRUE) : truncating string with embedded nuls
6: In readChar(file, size, TRUE) : truncating string with embedded nuls
7: In readChar(file, size, TRUE) : truncating string with embedded nuls
8: In readChar(file, size, TRUE) : truncating string with embedded nuls

save(arabidposis_pbsrep1, file = "arabidposis_pbsrep1_markers_tagm.rda")

load("C:/Users/OllyC/Desktop/2019-LOPIT-Projects/2020-Harriet-Arabidopsis-/RawData/arabidposis_pbsrep2.rda")
arab_psb_rep2_params <- readRDS(file = "C:/Users/OllyC/Desktop/2019-LOPIT-Projects/2020-Harriet-Arabidopsis-/RawData/mcmc_arabidposis_pbsrep2.rds")

#intial visualisation

fData(arabidposis_pbsrep2)$markers[fData(arabidposis_pbsrep2)$markers %in% c("cytosolic ribosomes", "extracellular")] <- "unknown"
plot2D(arabidposis_pbsrep2, grid = FALSE, fcol = "markers")
addLegend(arabidposis_pbsrep2, where = "topleft", cex = 0.7, ncol = 2, fcol = "markers")

out <- mcmc_get_outliers(arab_psb_rep2_params)

nChains <- length(arab_psb_rep2_params)
nChains

[1] 4

for (i in seq_len(nChains))
    plot(out[[i]], main = paste("Chain", i), auto.layout = FALSE, col = i)

NA

gelman.diag(out[c(1,2,3)], transform = FALSE)

Potential scale reduction factors:

     Point est. Upper C.I.
[1,]      0.999          1

arab_psb_rep2_params_pooled <- mcmc_pool_chains(arab_psb_rep2_params[c(1,2,3)])
arab_psb_rep2_params_pooled

Object of class "MCMCParams"
Method: TAGM.MCMC 
Number of chains: 1 

arab_psb_rep2_params_pooled <- tagmMcmcProcess(arab_psb_rep2_params_pooled)
arab_psb_rep2_params_pooled

Object of class "MCMCParams"
Method: TAGM.MCMC 
Number of chains: 1 
Summary available

arabidposis_pbsrep2 <- tagmPredict(object = arabidposis_pbsrep2,
                           params = arab_psb_rep2_params_pooled,
                           probJoint = TRUE,
                           fcol = "markers")
head(fData(arabidposis_pbsrep2))

NA

par(mfrow = c(1, 2))
plot2D(arabidposis_pbsrep2, fcol = "tagm.mcmc.allocation",
        cex = fData(arabidposis_pbsrep2)$tagm.mcmc.probability,
        main = "TAGM MCMC allocations")
addLegend(arabidposis_pbsrep2, fcol = "markers",
           where = "topleft", ncol = 2, cex = 0.6)

plot2D(arabidposis_pbsrep2, fcol = "tagm.mcmc.allocation",
        cex = fData(arabidposis_pbsrep2)$tagm.mcmc.mean.shannon,
        main = "Visualising global uncertainty")
addLegend(arabidposis_pbsrep2, fcol = "markers",
           where = "topleft", ncol = 2, cex = 0.6)

save(arab_psb_rep2_params_pooled, file = "arab_psb_rep2_params_pooled.rda")
save(arabidposis_pbsrep2, file = "arabidposis_pbsrep2_tagm.rda")

load("C:/Users/OllyC/Desktop/2019-LOPIT-Projects/2020-Harriet-Arabidopsis-/RawData/arabidposis_pbsrep2.rda")

Warning messages:
1: In readChar(file, size, TRUE) : truncating string with embedded nuls
2: In readChar(file, size, TRUE) : truncating string with embedded nuls
3: In readChar(file, size, TRUE) : truncating string with embedded nuls
4: In readChar(file, size, TRUE) : truncating string with embedded nuls
5: In readChar(file, size, TRUE) : truncating string with embedded nuls
6: In readChar(file, size, TRUE) : truncating string with embedded nuls
7: In readChar(file, size, TRUE) : truncating string with embedded nuls
8: In readChar(file, size, TRUE) : truncating string with embedded nuls
9: In readChar(file, size, TRUE) : truncating string with embedded nuls

arab_psb_rep2_params_markers2 <- readRDS(file = "C:/Users/OllyC/Desktop/2019-LOPIT-Projects/2020-Harriet-Arabidopsis-/RawData/mcmc_arabidposis_pbsrep2_markers2.rds")

#intial visualisation

fData(arabidposis_pbsrep2)$markers2[fData(arabidposis_pbsrep2)$markers2 %in% c("cytosolic ribosomes", "extracellular")] <- "unknown"
plot2D(arabidposis_pbsrep2, grid = FALSE, fcol = "markers2")
addLegend(arabidposis_pbsrep2, where = "topleft", cex = 0.7, ncol = 2, fcol = "markers")

out <- mcmc_get_outliers(arab_psb_rep2_params_markers2)

nChains <- length(arab_psb_rep2_params_markers2)
nChains

[1] 4

for (i in seq_len(nChains))
    plot(out[[i]], main = paste("Chain", i), auto.layout = FALSE, col = i)

NA

gelman.diag(out[c(1,2,4)], transform = FALSE)

Potential scale reduction factors:

     Point est. Upper C.I.
[1,]       1.02       1.04

arab_psb_rep2_params_markers2_pooled <- mcmc_pool_chains(arab_psb_rep2_params_markers2[c(1,2,3)])
arab_psb_rep2_params_markers2_pooled

Object of class "MCMCParams"
Method: TAGM.MCMC 
Number of chains: 1 

arab_psb_rep2_params_markers2_pooled <- tagmMcmcProcess(arab_psb_rep2_params_markers2_pooled)
arab_psb_rep2_params_markers2_pooled

Object of class "MCMCParams"
Method: TAGM.MCMC 
Number of chains: 1 
Summary available

arabidposis_pbsrep2 <- tagmPredict(object = arabidposis_pbsrep2,
                           params = arab_psb_rep2_params_markers2_pooled,
                           probJoint = TRUE,
                           fcol = "markers2")
head(fData(arabidposis_pbsrep2))

NA

par(mfrow = c(1, 2))
plot2D(arabidposis_pbsrep2, fcol = "tagm.mcmc.allocation",
        cex = fData(arabidposis_pbsrep2)$tagm.mcmc.probability,
        main = "TAGM MCMC allocations")
addLegend(arabidposis_pbsrep2, fcol = "markers2",
           where = "topleft", ncol = 2, cex = 0.6)

plot2D(arabidposis_pbsrep2, fcol = "tagm.mcmc.allocation",
        cex = fData(arabidposis_pbsrep2)$tagm.mcmc.mean.shannon,
        main = "Visualising global uncertainty")
addLegend(arabidposis_pbsrep2, fcol = "markers2",
           where = "topleft", ncol = 2, cex = 0.6)

save(arab_psb_rep2_params_pooled, file = "arab_psb_rep2_params_markers2_pooled.rda")
save(arabidposis_pbsrep2, file = "arabidposis_pbsrep2_markers2_tagm.rda")

Pbs rep 3

load("C:/Users/OllyC/Desktop/2019-LOPIT-Projects/2020-Harriet-Arabidopsis-/RawData/arabidposis_pbsrep3.rda")
arab_psb_rep3_params <- readRDS(file = "C:/Users/OllyC/Desktop/2019-LOPIT-Projects/2020-Harriet-Arabidopsis-/RawData/mcmc_arabidposis_pbsrep3.rds")

#intial visualisation

fData(arabidposis_pbsrep3)$markers[fData(arabidposis_pbsrep3)$markers %in% c("cytosolic ribosomes", "extracellular")] <- "unknown"
plot2D(arabidposis_pbsrep3, grid = FALSE, fcol = "markers")
addLegend(arabidposis_pbsrep3, where = "topleft", cex = 0.7, ncol = 2, fcol = "markers")

out <- mcmc_get_outliers(arab_psb_rep3_params)

nChains <- length(arab_psb_rep3_params)
nChains

[1] 4

for (i in seq_len(nChains))
    plot(out[[i]], main = paste("Chain", i), auto.layout = FALSE, col = i)

NA

gelman.diag(out[c(2,3)], transform = FALSE)

Potential scale reduction factors:

     Point est. Upper C.I.
[1,]          1          1

arab_psb_rep3_params_pooled <- mcmc_pool_chains(arab_psb_rep3_params[c(2,3)])
arab_psb_rep3_params_pooled

Object of class "MCMCParams"
Method: TAGM.MCMC 
Number of chains: 1 

arab_psb_rep3_params_pooled <- tagmMcmcProcess(arab_psb_rep3_params_pooled)
arab_psb_rep3_params_pooled

Object of class "MCMCParams"
Method: TAGM.MCMC 
Number of chains: 1 
Summary available

arabidposis_pbsrep3 <- tagmPredict(object = arabidposis_pbsrep3,
                           params = arab_psb_rep3_params_pooled,
                           probJoint = TRUE,
                           fcol = "markers")
head(fData(arabidposis_pbsrep3))

NA

par(mfrow = c(1, 2))
plot2D(arabidposis_pbsrep3, fcol = "tagm.mcmc.allocation",
        cex = fData(arabidposis_pbsrep3)$tagm.mcmc.probability,
        main = "TAGM MCMC allocations")
addLegend(arabidposis_pbsrep3, fcol = "markers",
           where = "topleft", ncol = 2, cex = 0.6)

plot2D(arabidposis_pbsrep3, fcol = "tagm.mcmc.allocation",
        cex = fData(arabidposis_pbsrep3)$tagm.mcmc.mean.shannon,
        main = "Visualising global uncertainty")
addLegend(arabidposis_pbsrep3, fcol = "markers",
           where = "topleft", ncol = 2, cex = 0.6)

save(arab_psb_rep3_params_pooled, file = "arab_psb_rep3_params_pooled.rda")
save(arabidposis_pbsrep3, file = "arabidposis_pbsrep3_tagm.rda")

Pbs rep 3 markers 2

load("C:/Users/OllyC/Desktop/2019-LOPIT-Projects/2020-Harriet-Arabidopsis-/RawData/arabidposis_pbsrep3.rda")
arab_psb_rep3_markers2_params <- readRDS(file = "C:/Users/OllyC/Desktop/2019-LOPIT-Projects/2020-Harriet-Arabidopsis-/RawData/mcmc_arabidposis_pbsrep3_markers2.rds")

#intial visualisation

fData(arabidposis_pbsrep3)$markers2[fData(arabidposis_pbsrep3)$markers2 %in% c("cytosolic ribosomes", "extracellular")] <- "unknown"
plot2D(arabidposis_pbsrep3, grid = FALSE, fcol = "markers")
addLegend(arabidposis_pbsrep3, where = "topleft", cex = 0.7, ncol = 2, fcol = "markers2")

out <- mcmc_get_outliers(arab_psb_rep3_markers2_params)

nChains <- length(arab_psb_rep3_markers2_params)
nChains

[1] 4

for (i in seq_len(nChains))
    plot(out[[i]], main = paste("Chain", i), auto.layout = FALSE, col = i)

NA

gelman.diag(out[c(3,4)], transform = FALSE)

Potential scale reduction factors:

     Point est. Upper C.I.
[1,]      0.999      0.999

arab_psb_rep3_markers2_params_pooled <- mcmc_pool_chains(arab_psb_rep3_markers2_params[c(3,4)])
arab_psb_rep3_markers2_params_pooled

Object of class "MCMCParams"
Method: TAGM.MCMC 
Number of chains: 1 

arab_psb_rep3_markers2_params_pooled <- tagmMcmcProcess(arab_psb_rep3_markers2_params_pooled)
arab_psb_rep3_markers2_params_pooled

Object of class "MCMCParams"
Method: TAGM.MCMC 
Number of chains: 1 
Summary available

arabidposis_pbsrep3 <- tagmPredict(object = arabidposis_pbsrep3,
                           params = arab_psb_rep3_markers2_params_pooled,
                           probJoint = TRUE,
                           fcol = "markers2")
head(fData(arabidposis_pbsrep3))

NA

par(mfrow = c(1, 2))
plot2D(arabidposis_pbsrep3, fcol = "tagm.mcmc.allocation",
        cex = fData(arabidposis_pbsrep3)$tagm.mcmc.probability,
        main = "TAGM MCMC allocations")
addLegend(arabidposis_pbsrep3, fcol = "markers2",
           where = "topleft", ncol = 2, cex = 0.6)

plot2D(arabidposis_pbsrep3, fcol = "tagm.mcmc.allocation",
        cex = fData(arabidposis_pbsrep3)$tagm.mcmc.mean.shannon,
        main = "Visualising global uncertainty")
addLegend(arabidposis_pbsrep3, fcol = "markers2",
           where = "topleft", ncol = 2, cex = 0.6)

save(arab_psb_rep3_markers2_params_pooled, file = "arab_psb_rep3_markers2_params_pooled.rda")
save(arabidposis_pbsrep3, file = "arabidposis_pbsrep3_markers2_tagm.rda")

Pbs rep 4

load("C:/Users/OllyC/Desktop/2019-LOPIT-Projects/2020-Harriet-Arabidopsis-/RawData/arabidposis_pbsrep4.rda")
arab_psb_rep4_params <- readRDS(file = "C:/Users/OllyC/Desktop/2019-LOPIT-Projects/2020-Harriet-Arabidopsis-/RawData/mcmc_arabidposis_pbsrep4.rds")

#intial visualisation

fData(arabidposis_pbsrep4)$markers[fData(arabidposis_pbsrep4)$markers %in% c("cytosolic ribosomes", "extracellular")] <- "unknown"
plot2D(arabidposis_pbsrep4, grid = FALSE, fcol = "markers")
addLegend(arabidposis_pbsrep4, where = "topleft", cex = 0.7, ncol = 2, fcol = "markers")

out <- mcmc_get_outliers(arab_psb_rep4_params)

nChains <- length(arab_psb_rep4_params)
nChains

[1] 4

for (i in seq_len(nChains))
    plot(out[[i]], main = paste("Chain", i), auto.layout = FALSE, col = i)

NA

gelman.diag(out[c(1,2)], transform = FALSE)

Potential scale reduction factors:

     Point est. Upper C.I.
[1,]      0.999          1

arab_psb_rep4_params_pooled <- mcmc_pool_chains(arab_psb_rep4_params[c(1,2)])
arab_psb_rep4_params_pooled

Object of class "MCMCParams"
Method: TAGM.MCMC 
Number of chains: 1 

arab_psb_rep4_params_pooled <- tagmMcmcProcess(arab_psb_rep4_params_pooled)
arab_psb_rep4_params_pooled

Object of class "MCMCParams"
Method: TAGM.MCMC 
Number of chains: 1 
Summary available

arabidposis_pbsrep4 <- tagmPredict(object = arabidposis_pbsrep4,
                           params = arab_psb_rep4_params_pooled,
                           probJoint = TRUE,
                           fcol = "markers")
head(fData(arabidposis_pbsrep4))

NA

par(mfrow = c(1, 2))
plot2D(arabidposis_pbsrep4, fcol = "tagm.mcmc.allocation",
        cex = fData(arabidposis_pbsrep4)$tagm.mcmc.probability,
        main = "TAGM MCMC allocations")
addLegend(arabidposis_pbsrep4, fcol = "markers",
           where = "topleft", ncol = 2, cex = 0.6)

plot2D(arabidposis_pbsrep4, fcol = "tagm.mcmc.allocation",
        cex = fData(arabidposis_pbsrep4)$tagm.mcmc.mean.shannon,
        main = "Visualising global uncertainty")
addLegend(arabidposis_pbsrep4, fcol = "markers",
           where = "topleft", ncol = 2, cex = 0.6)

save(arab_psb_rep4_params_pooled, file = "arab_psb_rep4_pooled.rda")
save(arabidposis_pbsrep4, file = "arabidposis_pbsrep4_tagm.rda")

arabidopsis pbs rep 4 markers2

load("C:/Users/OllyC/Desktop/2019-LOPIT-Projects/2020-Harriet-Arabidopsis-/RawData/arabidposis_pbsrep4.rda")
arab_psb_rep4_params_markers2 <- readRDS(file = "C:/Users/OllyC/Desktop/2019-LOPIT-Projects/2020-Harriet-Arabidopsis-/RawData/mcmc_arabidposis_pbsrep4_markers2.rds")

#intial visualisation

fData(arabidposis_pbsrep4)$markers2[fData(arabidposis_pbsrep4)$markers2 %in% c("cytosolic ribosomes", "extracellular")] <- "unknown"
plot2D(arabidposis_pbsrep4, grid = FALSE, fcol = "markers2")
addLegend(arabidposis_pbsrep4, where = "topleft", cex = 0.7, ncol = 2, fcol = "markers2")

out <- mcmc_get_outliers(arab_psb_rep4_params_markers2)

nChains <- length(arab_psb_rep4_params_markers2)
nChains

[1] 4

for (i in seq_len(nChains))
    plot(out[[i]], main = paste("Chain", i), auto.layout = FALSE, col = i)

NA

gelman.diag(out[c(3,4)], transform = FALSE)

Potential scale reduction factors:

     Point est. Upper C.I.
[1,]          1       1.01

arab_psb_rep4_params_markers2_pooled <- mcmc_pool_chains(arab_psb_rep4_params_markers2[c(3,4)])
arab_psb_rep4_params_markers2_pooled

Object of class "MCMCParams"
Method: TAGM.MCMC 
Number of chains: 1 

arab_psb_rep4_params_markers2_pooled <- tagmMcmcProcess(arab_psb_rep4_params_markers2_pooled)
arab_psb_rep4_params_markers2_pooled

Object of class "MCMCParams"
Method: TAGM.MCMC 
Number of chains: 1 
Summary available

arabidposis_pbsrep4 <- tagmPredict(object = arabidposis_pbsrep4,
                           params = arab_psb_rep4_params_markers2_pooled,
                           probJoint = TRUE,
                           fcol = "markers2")
head(fData(arabidposis_pbsrep4))

NA

par(mfrow = c(1, 2))
plot2D(arabidposis_pbsrep4, fcol = "tagm.mcmc.allocation",
        cex = fData(arabidposis_pbsrep4)$tagm.mcmc.probability,
        main = "TAGM MCMC allocations")
addLegend(arabidposis_pbsrep4, fcol = "markers2",
           where = "topleft", ncol = 2, cex = 0.6)

plot2D(arabidposis_pbsrep4, fcol = "tagm.mcmc.allocation",
        cex = fData(arabidposis_pbsrep4)$tagm.mcmc.mean.shannon,
        main = "Visualising global uncertainty")
addLegend(arabidposis_pbsrep4, fcol = "markers2",
           where = "topleft", ncol = 2, cex = 0.6)

save(arab_psb_rep4_params_markers2_pooled, file = "arab_psb_rep4_params_markers2_pooled.rda")
save(arabidposis_pbsrep4, file = "arabidposis_pbsrep4_markers2_tagm.rda")

PBS combined replicates

load("C:/Users/OllyC/Desktop/2019-LOPIT-Projects/2020-Harriet-Arabidopsis-/RawData/arabidposis_pbs.rda")
arab_psb_params <- readRDS(file = "C:/Users/OllyC/Desktop/2019-LOPIT-Projects/2020-Harriet-Arabidopsis-/RawData/mcmc_arabidposis_pbs.rds")

#intial visualisation

fData(arabidposis_pbs)$markers[fData(arabidposis_pbs)$markers %in% c("cytosolic ribosomes", "extracellular")] <- "unknown"
plot2D(arabidposis_pbs, grid = FALSE, fcol = "markers")
addLegend(arabidposis_pbsrep4, where = "topleft", cex = 0.7, ncol = 2, fcol = "markers")

out <- mcmc_get_outliers(arab_psb_params)

nChains <- length(arab_psb_params)
nChains

[1] 4

for (i in seq_len(nChains))
    plot(out[[i]], main = paste("Chain", i), auto.layout = FALSE, col = i)

NA

gelman.diag(out[c(1,2,3,4)], transform = FALSE)

Potential scale reduction factors:

     Point est. Upper C.I.
[1,]       1.02       1.05

arab_psb_params_pooled <- mcmc_pool_chains(arab_psb_params[c(1,2,3,4)])
arab_psb_params_pooled

Object of class "MCMCParams"
Method: TAGM.MCMC 
Number of chains: 1 

arab_psb_params_pooled <- tagmMcmcProcess(arab_psb_params_pooled)
arab_psb_params_pooled

Object of class "MCMCParams"
Method: TAGM.MCMC 
Number of chains: 1 
Summary available

arabidposis_pbs <- tagmPredict(object = arabidposis_pbs,
                           params = arab_psb_params_pooled,
                           probJoint = TRUE,
                           fcol = "markers")
head(fData(arabidposis_pbs))

NA

par(mfrow = c(1, 2))
plot2D(arabidposis_pbs, fcol = "tagm.mcmc.allocation",
        cex = fData(arabidposis_pbs)$tagm.mcmc.probability,
        main = "TAGM MCMC allocations")
addLegend(arabidposis_pbsrep4, fcol = "markers",
           where = "topleft", ncol = 2, cex = 0.6)

plot2D(arabidposis_pbs, fcol = "tagm.mcmc.allocation",
        cex = fData(arabidposis_pbs)$tagm.mcmc.mean.shannon,
        main = "Visualising global uncertainty")
addLegend(arabidposis_pbsrep4, fcol = "markers",
           where = "topleft", ncol = 2, cex = 0.6)

save(arab_psb_params_pooled, file = "arab_psb_params_pooled.rda")
save(arabidposis_pbs, file = "arabidposis_pbs_tagm.rda")

Pbs combined markers2

load("C:/Users/OllyC/Desktop/2019-LOPIT-Projects/2020-Harriet-Arabidopsis-/RawData/arabidposis_pbs.rda")
arab_psb_params_markers2 <- readRDS(file = "C:/Users/OllyC/Desktop/2019-LOPIT-Projects/2020-Harriet-Arabidopsis-/RawData/mcmc_arabidposis_pbs_markers2.rds")

#intial visualisation

fData(arabidposis_pbs)$markers2[fData(arabidposis_pbs)$markers2 %in% c("cytosolic ribosomes", "extracellular")] <- "unknown"
plot2D(arabidposis_pbs, grid = FALSE, fcol = "markers2")
addLegend(arabidposis_pbsrep4, where = "topleft", cex = 0.7, ncol = 2, fcol = "markers2")

out <- mcmc_get_outliers(arab_psb_params_markers2)

nChains <- length(arab_psb_params_markers2)
nChains

[1] 4

for (i in seq_len(nChains))
    plot(out[[i]], main = paste("Chain", i), auto.layout = FALSE, col = i)

NA

gelman.diag(out[c(2,3)], transform = FALSE)

Potential scale reduction factors:

     Point est. Upper C.I.
[1,]       1.06       1.16

arab_psb_params_markers2_pooled <- mcmc_pool_chains(arab_psb_params_markers2[c(2,3)])
arab_psb_params_markers2_pooled

Object of class "MCMCParams"
Method: TAGM.MCMC 
Number of chains: 1 

arab_psb_params_markers2_pooled <- tagmMcmcProcess(arab_psb_params_markers2_pooled)
arab_psb_params_markers2_pooled

Object of class "MCMCParams"
Method: TAGM.MCMC 
Number of chains: 1 
Summary available

arabidposis_pbs <- tagmPredict(object = arabidposis_pbs,
                           params = arab_psb_params_markers2_pooled,
                           probJoint = TRUE,
                           fcol = "markers2")
head(fData(arabidposis_pbs))

NA

par(mfrow = c(1, 2))
plot2D(arabidposis_pbs, fcol = "tagm.mcmc.allocation",
        cex = fData(arabidposis_pbs)$tagm.mcmc.probability,
        main = "TAGM MCMC allocations")
addLegend(arabidposis_pbsrep4, fcol = "markers2",
           where = "topleft", ncol = 2, cex = 0.6)

plot2D(arabidposis_pbs, fcol = "tagm.mcmc.allocation",
        cex = fData(arabidposis_pbs)$tagm.mcmc.mean.shannon,
        main = "Visualising global uncertainty")
addLegend(arabidposis_pbsrep4, fcol = "markers2",
           where = "topleft", ncol = 2, cex = 0.6)

save(arab_psb_params_markers2_pooled, file = "arab_psb_params_markers2_pooled.rda")
save(arabidposis_pbs, file = "arabidposis_pbs_markers2_tagm.rda")

CO3 rep 1

load("C:/Users/OllyC/Desktop/2019-LOPIT-Projects/2020-Harriet-Arabidopsis-/RawData/arabidposis_co3.rda")
arab_co3rep1_params <- readRDS(file = "C:/Users/OllyC/Desktop/2019-LOPIT-Projects/2020-Harriet-Arabidopsis-/RawData/mcmc_arabidposis_co3rep1.rds")

#intial visualisation

fData(arabidposis_co3rep1)$markers[fData(arabidposis_co3rep1)$markers %in% c("cytosolic ribosomes", "extracellular")] <- "unknown"
plot2D(arabidposis_co3rep1, grid = FALSE, fcol = "markers")
addLegend(arabidposis_co3rep1, where = "topleft", cex = 0.7, ncol = 2, fcol = "markers")

out <- mcmc_get_outliers(arab_co3rep1_params)

nChains <- length(arab_co3rep1_params)
nChains

[1] 4

for (i in seq_len(nChains))
    plot(out[[i]], main = paste("Chain", i), auto.layout = FALSE, col = i)

NA

gelman.diag(out[c(2,3)], transform = FALSE)

Potential scale reduction factors:

     Point est. Upper C.I.
[1,]       1.01       1.04

arab_co3rep1_params_pooled <- mcmc_pool_chains(arab_co3rep1_params[c(2,3)])
arab_co3rep1_params_pooled

Object of class "MCMCParams"
Method: TAGM.MCMC 
Number of chains: 1 

arab_co3rep1_params_pooled <- tagmMcmcProcess(arab_co3rep1_params_pooled)
arab_co3rep1_params_pooled

Object of class "MCMCParams"
Method: TAGM.MCMC 
Number of chains: 1 
Summary available

arabidposis_co3rep1 <- tagmPredict(object = arabidposis_co3rep1,
                           params = arab_co3rep1_params_pooled,
                           probJoint = TRUE,
                           fcol = "markers")
head(fData(arabidposis_co3rep1))

NA

par(mfrow = c(1, 2))
plot2D(arabidposis_co3rep1, fcol = "tagm.mcmc.allocation",
        cex = fData(arabidposis_co3rep1)$tagm.mcmc.probability,
        main = "TAGM MCMC allocations")
addLegend(arabidposis_pbsrep4, fcol = "markers",
           where = "topleft", ncol = 2, cex = 0.6)

plot2D(arabidposis_co3rep1, fcol = "tagm.mcmc.allocation",
        cex = fData(arabidposis_co3rep1)$tagm.mcmc.mean.shannon,
        main = "Visualising global uncertainty")
addLegend(arabidposis_pbsrep4, fcol = "markers",
           where = "topleft", ncol = 2, cex = 0.6)

save(arab_co3rep1_params_pooled, file = "arab_co3rep1_params_pooled.rda")
save(arabidposis_co3rep1, file = "arabidposis_co3rep1_tagm.rda")

CO3 rep 1 marker2

load("C:/Users/OllyC/Desktop/2019-LOPIT-Projects/2020-Harriet-Arabidopsis-/RawData/arabidposis_co3rep1.rda")
arab_co3rep1_marker2_params <- readRDS(file = "C:/Users/OllyC/Desktop/2019-LOPIT-Projects/2020-Harriet-Arabidopsis-/RawData/mcmc_arabidposis_co3rep1_markers2.rds")

#intial visualisation

fData(arabidposis_co3rep1)$markers2[fData(arabidposis_co3rep1)$markers2 %in% c("cytosolic ribosomes", "extracellular")] <- "unknown"
plot2D(arabidposis_co3rep1, grid = FALSE, fcol = "markers2")
addLegend(arabidposis_co3rep1, where = "topleft", cex = 0.7, ncol = 2, fcol = "markers2")

out <- mcmc_get_outliers(arab_co3rep1_marker2_params)

nChains <- length(arab_co3rep1_marker2_params)
nChains

[1] 4

for (i in seq_len(nChains))
    plot(out[[i]], main = paste("Chain", i), auto.layout = FALSE, col = i)

NA

gelman.diag(out[c(2,3,4)], transform = FALSE)

Potential scale reduction factors:

     Point est. Upper C.I.
[1,]       1.02       1.05

arab_co3rep1_marker2_params_pooled <- mcmc_pool_chains(arab_co3rep1_marker2_params[c(2,3,4)])
arab_co3rep1_marker2_params_pooled

Object of class "MCMCParams"
Method: TAGM.MCMC 
Number of chains: 1 

arab_co3rep1_marker2_params_pooled <- tagmMcmcProcess(arab_co3rep1_marker2_params_pooled)
arab_co3rep1_marker2_params_pooled

Object of class "MCMCParams"
Method: TAGM.MCMC 
Number of chains: 1 
Summary available

arabidposis_co3rep1 <- tagmPredict(object = arabidposis_co3rep1,
                           params = arab_co3rep1_marker2_params_pooled,
                           probJoint = TRUE,
                           fcol = "markers2")
head(fData(arabidposis_co3rep1))

NA

par(mfrow = c(1, 2))
plot2D(arabidposis_co3rep1, fcol = "tagm.mcmc.allocation",
        cex = fData(arabidposis_co3rep1)$tagm.mcmc.probability,
        main = "TAGM MCMC allocations")
addLegend(arabidposis_co3rep1, fcol = "markers2",
           where = "topleft", ncol = 2, cex = 0.6)

plot2D(arabidposis_co3rep1, fcol = "tagm.mcmc.allocation",
        cex = fData(arabidposis_co3rep1)$tagm.mcmc.mean.shannon,
        main = "Visualising global uncertainty")
addLegend(arabidposis_co3rep1, fcol = "markers2",
           where = "topleft", ncol = 2, cex = 0.6)

save(arab_co3rep1_marker2_params_pooled, file = "arab_co3rep1_marker2_params_pooled.rda")
save(arabidposis_co3rep1, file = "arabidposis_co3rep1_markers2_tagm.rda")

co3 rep2

load("C:/Users/OllyC/Desktop/2019-LOPIT-Projects/2020-Harriet-Arabidopsis-/RawData/arabidposis_co3rep2.rda")
arab_co3rep2_params <- readRDS(file = "C:/Users/OllyC/Desktop/2019-LOPIT-Projects/2020-Harriet-Arabidopsis-/RawData/mcmc_arabidposis_co3rep2.rds")

#intial visualisation

fData(arabidposis_co3rep2)$markers[fData(arabidposis_co3rep2)$markers %in% c("cytosolic ribosomes", "extracellular")] <- "unknown"
plot2D(arabidposis_co3rep2, grid = FALSE, fcol = "markers")
addLegend(arabidposis_co3rep2, where = "topleft", cex = 0.7, ncol = 2, fcol = "markers")

out <- mcmc_get_outliers(arab_co3rep2_params)

nChains <- length(arab_co3rep2_params)
nChains

[1] 4

for (i in seq_len(nChains))
    plot(out[[i]], main = paste("Chain", i), auto.layout = FALSE, col = i)

NA

gelman.diag(out[c(1,2)], transform = FALSE)

Potential scale reduction factors:

     Point est. Upper C.I.
[1,]          1          1

arab_co3rep2_params_pooled <- mcmc_pool_chains(arab_co3rep2_params[c(1,2)])
arab_co3rep2_params_pooled

Object of class "MCMCParams"
Method: TAGM.MCMC 
Number of chains: 1 

arab_co3rep2_params_pooled <- tagmMcmcProcess(arab_co3rep2_params_pooled)
arab_co3rep2_params_pooled

Object of class "MCMCParams"
Method: TAGM.MCMC 
Number of chains: 1 
Summary available

arabidposis_co3rep2 <- tagmPredict(object = arabidposis_co3rep2,
                           params = arab_co3rep2_params_pooled,
                           probJoint = TRUE,
                           fcol = "markers")
head(fData(arabidposis_co3rep2))

NA

par(mfrow = c(1, 2))
plot2D(arabidposis_co3rep2, fcol = "tagm.mcmc.allocation",
        cex = fData(arabidposis_co3rep2)$tagm.mcmc.probability,
        main = "TAGM MCMC allocations")
addLegend(arabidposis_co3rep2, fcol = "markers",
           where = "topleft", ncol = 2, cex = 0.6)

plot2D(arabidposis_co3rep2, fcol = "tagm.mcmc.allocation",
        cex = fData(arabidposis_co3rep2)$tagm.mcmc.mean.shannon,
        main = "Visualising global uncertainty")
addLegend(arabidposis_co3rep2, fcol = "markers",
           where = "topleft", ncol = 2, cex = 0.6)

save(arab_co3rep2_params_pooled, file = "arab_co3rep2_params_pooled.rda")
save(arabidposis_co3rep2, file = "arabidposis_co3rep2_tagm.rda")

CO3 rep 2 markers2

load("C:/Users/OllyC/Desktop/2019-LOPIT-Projects/2020-Harriet-Arabidopsis-/RawData/arabidposis_co3rep2.rda")
arab_co3rep2_markers2_params <- readRDS(file = "C:/Users/OllyC/Desktop/2019-LOPIT-Projects/2020-Harriet-Arabidopsis-/RawData/mcmc_arabidposis_co3rep2_markers2.rds")

#intial visualisation

fData(arabidposis_co3rep2)$markers2[fData(arabidposis_co3rep2)$markers2 %in% c("cytosolic ribosomes", "extracellular")] <- "unknown"
plot2D(arabidposis_co3rep2, grid = FALSE, fcol = "markers2")
addLegend(arabidposis_co3rep2, where = "topleft", cex = 0.7, ncol = 2, fcol = "markers2")

out <- mcmc_get_outliers(arab_co3rep2_markers2_params)

nChains <- length(arab_co3rep2_markers2_params)
nChains

[1] 4

for (i in seq_len(nChains))
    plot(out[[i]], main = paste("Chain", i), auto.layout = FALSE, col = i)

NA

gelman.diag(out[c(2,3)], transform = FALSE)

Potential scale reduction factors:

     Point est. Upper C.I.
[1,]       1.04       1.12

arab_co3rep2_markers2_params_pooled <- mcmc_pool_chains(arab_co3rep2_markers2_params[c(2,3)])
arab_co3rep2_markers2_params_pooled

Object of class "MCMCParams"
Method: TAGM.MCMC 
Number of chains: 1 

arab_co3rep2_markers2_params_pooled <- tagmMcmcProcess(arab_co3rep2_markers2_params_pooled)
arab_co3rep2_markers2_params_pooled

Object of class "MCMCParams"
Method: TAGM.MCMC 
Number of chains: 1 
Summary available

arabidposis_co3rep2 <- tagmPredict(object = arabidposis_co3rep2,
                           params = arab_co3rep2_markers2_params_pooled,
                           probJoint = TRUE,
                           fcol = "markers2")
head(fData(arabidposis_co3rep2))

NA

par(mfrow = c(1, 2))
plot2D(arabidposis_co3rep2, fcol = "tagm.mcmc.allocation",
        cex = fData(arabidposis_co3rep2)$tagm.mcmc.probability,
        main = "TAGM MCMC allocations")
addLegend(arabidposis_co3rep2, fcol = "markers2",
           where = "topleft", ncol = 2, cex = 0.6)

plot2D(arabidposis_co3rep2, fcol = "tagm.mcmc.allocation",
        cex = fData(arabidposis_co3rep2)$tagm.mcmc.mean.shannon,
        main = "Visualising global uncertainty")
addLegend(arabidposis_co3rep2, fcol = "markers2",
           where = "topleft", ncol = 2, cex = 0.6)

save(arab_co3rep2_markers2_params_pooled, file = "arab_co3rep2_markers2_params_pooled.rda")
save(arabidposis_co3rep2, file = "arabidposis_co3rep2_markers2_tagm.rda")

CO3 rep 3

load("C:/Users/OllyC/Desktop/2019-LOPIT-Projects/2020-Harriet-Arabidopsis-/RawData/arabidposis_co3rep3.rda")
arab_co3rep3_params <- readRDS(file = "C:/Users/OllyC/Desktop/2019-LOPIT-Projects/2020-Harriet-Arabidopsis-/RawData/mcmc_arabidposis_co3rep3.rds")

#intial visualisation

fData(arabidposis_co3rep3)$markers[fData(arabidposis_co3rep3)$markers %in% c("cytosolic ribosomes", "extracellular")] <- "unknown"
plot2D(arabidposis_co3rep3, grid = FALSE, fcol = "markers")
addLegend(arabidposis_co3rep3, where = "topleft", cex = 0.7, ncol = 2, fcol = "markers")

out <- mcmc_get_outliers(arab_co3rep3_params)

nChains <- length(arab_co3rep3_params)
nChains

[1] 4

for (i in seq_len(nChains))
    plot(out[[i]], main = paste("Chain", i), auto.layout = FALSE, col = i)

NA

gelman.diag(out[c(2,3)], transform = FALSE)

Potential scale reduction factors:

     Point est. Upper C.I.
[1,]       1.01       1.04

arab_co3rep3_params_pooled <- mcmc_pool_chains(arab_co3rep3_params[c(2,3)])
arab_co3rep3_params_pooled

Object of class "MCMCParams"
Method: TAGM.MCMC 
Number of chains: 1 

arab_co3rep3_params_pooled <- tagmMcmcProcess(arab_co3rep3_params_pooled)
arab_co3rep3_params_pooled

Object of class "MCMCParams"
Method: TAGM.MCMC 
Number of chains: 1 
Summary available

arabidposis_co3rep3 <- tagmPredict(object = arabidposis_co3rep3,
                           params = arab_co3rep3_params_pooled,
                           probJoint = TRUE,
                           fcol = "markers")
head(fData(arabidposis_co3rep3))

NA

par(mfrow = c(1, 2))
plot2D(arabidposis_co3rep3, fcol = "tagm.mcmc.allocation",
        cex = fData(arabidposis_co3rep3)$tagm.mcmc.probability,
        main = "TAGM MCMC allocations")
addLegend(arabidposis_co3rep3, fcol = "markers",
           where = "topleft", ncol = 2, cex = 0.6)

plot2D(arabidposis_co3rep3, fcol = "tagm.mcmc.allocation",
        cex = fData(arabidposis_co3rep3)$tagm.mcmc.mean.shannon,
        main = "Visualising global uncertainty")
addLegend(arabidposis_co3rep2, fcol = "markers",
           where = "topleft", ncol = 2, cex = 0.6)

save(arab_co3rep3_params_pooled, file = "arab_co3rep3_params_pooled.rda")
save(arabidposis_co3rep3, file = "arabidposis_co3rep3_tagm.rda")

Co3 rep3 markers 2

load("C:/Users/OllyC/Desktop/2019-LOPIT-Projects/2020-Harriet-Arabidopsis-/RawData/arabidposis_co3rep3.rda")
arab_co3rep3_markers2_params <- readRDS(file = "C:/Users/OllyC/Desktop/2019-LOPIT-Projects/2020-Harriet-Arabidopsis-/RawData/mcmc_arabidposis_co3rep3_markers2.rds")

#intial visualisation

fData(arabidposis_co3rep3)$markers2[fData(arabidposis_co3rep3)$markers2 %in% c("cytosolic ribosomes", "extracellular")] <- "unknown"
plot2D(arabidposis_co3rep3, grid = FALSE, fcol = "markers2")
addLegend(arabidposis_co3rep3, where = "topleft", cex = 0.7, ncol = 2, fcol = "markers2")

out <- mcmc_get_outliers(arab_co3rep3_markers2_params)

nChains <- length(arab_co3rep3_markers2_params)
nChains

[1] 4

for (i in seq_len(nChains))
    plot(out[[i]], main = paste("Chain", i), auto.layout = FALSE, col = i)

NA

gelman.diag(out[c(2,3)], transform = FALSE)

Potential scale reduction factors:

     Point est. Upper C.I.
[1,]       1.01       1.03

arab_co3rep3_markers2_params_pooled <- mcmc_pool_chains(arab_co3rep3_markers2_params[c(2,3)])
arab_co3rep3_markers2_params_pooled

Object of class "MCMCParams"
Method: TAGM.MCMC 
Number of chains: 1 

arab_co3rep3_markers2_params_pooled <- tagmMcmcProcess(arab_co3rep3_markers2_params_pooled)
arab_co3rep3_markers2_params_pooled

Object of class "MCMCParams"
Method: TAGM.MCMC 
Number of chains: 1 
Summary available

arabidposis_co3rep3 <- tagmPredict(object = arabidposis_co3rep3,
                           params = arab_co3rep3_markers2_params_pooled,
                           probJoint = TRUE,
                           fcol = "markers2")
head(fData(arabidposis_co3rep3))

NA

par(mfrow = c(1, 2))
plot2D(arabidposis_co3rep3, fcol = "tagm.mcmc.allocation",
        cex = fData(arabidposis_co3rep3)$tagm.mcmc.probability,
        main = "TAGM MCMC allocations")
addLegend(arabidposis_co3rep3, fcol = "markers2",
           where = "topleft", ncol = 2, cex = 0.6)

plot2D(arabidposis_co3rep3, fcol = "tagm.mcmc.allocation",
        cex = fData(arabidposis_co3rep3)$tagm.mcmc.mean.shannon,
        main = "Visualising global uncertainty")
addLegend(arabidposis_co3rep2, fcol = "markers2",
           where = "topleft", ncol = 2, cex = 0.6)

save( arab_co3rep3_markers2_params_pooled, file = " arab_co3rep3_markers2_params_pooled.rda")
save(arabidposis_co3rep3, file = "arabidposis_co3rep3_markers2_tagm.rda")

co3 rep 4

load("C:/Users/OllyC/Desktop/2019-LOPIT-Projects/2020-Harriet-Arabidopsis-/RawData/arabidposis_co3rep4.rda")
arab_co3rep4_params <- readRDS(file = "C:/Users/OllyC/Desktop/2019-LOPIT-Projects/2020-Harriet-Arabidopsis-/RawData/mcmc_arabidposis_co3rep4.rds")

#intial visualisation

fData(arabidposis_co3rep4)$markers[fData(arabidposis_co3rep4)$markers %in% c("cytosolic ribosomes", "extracellular")] <- "unknown"
plot2D(arabidposis_co3rep4, grid = FALSE, fcol = "markers")
addLegend(arabidposis_co3rep4, where = "topleft", cex = 0.7, ncol = 2, fcol = "markers")

out <- mcmc_get_outliers(arab_co3rep4_params)

nChains <- length(arab_co3rep4_params)
nChains

[1] 4

for (i in seq_len(nChains))
    plot(out[[i]], main = paste("Chain", i), auto.layout = FALSE, col = i)

NA

gelman.diag(out[c(1,2)], transform = FALSE)

Potential scale reduction factors:

     Point est. Upper C.I.
[1,]      0.999          1

arab_co3rep4_params_pooled <- mcmc_pool_chains(arab_co3rep4_params[c(1,2)])
arab_co3rep4_params_pooled

Object of class "MCMCParams"
Method: TAGM.MCMC 
Number of chains: 1 

arab_co3rep4_params_pooled <- tagmMcmcProcess(arab_co3rep4_params_pooled)
arab_co3rep4_params_pooled

Object of class "MCMCParams"
Method: TAGM.MCMC 
Number of chains: 1 
Summary available

arabidposis_co3rep4 <- tagmPredict(object = arabidposis_co3rep4,
                           params = arab_co3rep4_params_pooled,
                           probJoint = TRUE,
                           fcol = "markers")
head(fData(arabidposis_co3rep4))

NA

par(mfrow = c(1, 2))
plot2D(arabidposis_co3rep4, fcol = "tagm.mcmc.allocation",
        cex = fData(arabidposis_co3rep4)$tagm.mcmc.probability,
        main = "TAGM MCMC allocations")
addLegend(arabidposis_co3rep4, fcol = "markers",
           where = "topleft", ncol = 2, cex = 0.6)

plot2D(arabidposis_co3rep4, fcol = "tagm.mcmc.allocation",
        cex = fData(arabidposis_co3rep4)$tagm.mcmc.mean.shannon,
        main = "Visualising global uncertainty")
addLegend(arabidposis_co3rep4, fcol = "markers",
           where = "topleft", ncol = 2, cex = 0.6)

save( arab_co3rep4_params_pooled, file = " arab_co3rep4_params_pooled.rda")
save(arabidposis_co3rep4, file = "arabidposis_co3rep4_tagm.rda")

Co3 rep 4 markers 2

load("C:/Users/OllyC/Desktop/2019-LOPIT-Projects/2020-Harriet-Arabidopsis-/RawData/arabidposis_co3rep4.rda")
arab_co3rep4_markers2_params <- readRDS(file = "C:/Users/OllyC/Desktop/2019-LOPIT-Projects/2020-Harriet-Arabidopsis-/RawData/mcmc_arabidposis_co3rep4_markers2.rds")

#intial visualisation

fData(arabidposis_co3rep4)$markers2[fData(arabidposis_co3rep4)$markers2 %in% c("cytosolic ribosomes", "extracellular")] <- "unknown"
plot2D(arabidposis_co3rep4, grid = FALSE, fcol = "markers2")
addLegend(arabidposis_co3rep4, where = "topleft", cex = 0.7, ncol = 2, fcol = "markers2")

out <- mcmc_get_outliers(arab_co3rep4_markers2_params)

nChains <- length(arab_co3rep4_markers2_params)
nChains

[1] 4

for (i in seq_len(nChains))
    plot(out[[i]], main = paste("Chain", i), auto.layout = FALSE, col = i)

NA

gelman.diag(out[c(2,3,4)], transform = FALSE)

Potential scale reduction factors:

     Point est. Upper C.I.
[1,]       1.01       1.04

arab_co3rep4_markers2_params_pooled <- mcmc_pool_chains(arab_co3rep4_markers2_params[c(2,3,4)])
arab_co3rep4_markers2_params_pooled

Object of class "MCMCParams"
Method: TAGM.MCMC 
Number of chains: 1 

arab_co3rep4_markers2_params_pooled <- tagmMcmcProcess(arab_co3rep4_markers2_params_pooled)
arab_co3rep4_markers2_params_pooled

Object of class "MCMCParams"
Method: TAGM.MCMC 
Number of chains: 1 
Summary available

arabidposis_co3rep4 <- tagmPredict(object = arabidposis_co3rep4,
                           params = arab_co3rep4_markers2_params_pooled,
                           probJoint = TRUE,
                           fcol = "markers2")
head(fData(arabidposis_co3rep4))

NA

par(mfrow = c(1, 2))
plot2D(arabidposis_co3rep4, fcol = "tagm.mcmc.allocation",
        cex = fData(arabidposis_co3rep4)$tagm.mcmc.probability,
        main = "TAGM MCMC allocations")
addLegend(arabidposis_co3rep4, fcol = "markers2",
           where = "topleft", ncol = 2, cex = 0.6)

plot2D(arabidposis_co3rep4, fcol = "tagm.mcmc.allocation",
        cex = fData(arabidposis_co3rep4)$tagm.mcmc.mean.shannon,
        main = "Visualising global uncertainty")
addLegend(arabidposis_co3rep4, fcol = "markers2",
           where = "topleft", ncol = 2, cex = 0.6)

save(arab_co3rep4_markers2_params_pooled, file = "arab_co3rep4_markers2_params_pooled.rda")
save(arabidposis_co3rep4, file = "arabidposis_co3rep4_markers2_tagm.rda")

Co3 combined reps

load("C:/Users/OllyC/Desktop/2019-LOPIT-Projects/2020-Harriet-Arabidopsis-/RawData/arabidposis_co3rep4.rda")
arab_co3rep4_params <- readRDS(file = "C:/Users/OllyC/Desktop/2019-LOPIT-Projects/2020-Harriet-Arabidopsis-/RawData/mcmc_arabidposis_co3rep4.rds")

#intial visualisation

fData(arabidposis_co3rep4)$markers[fData(arabidposis_co3rep4)$markers %in% c("cytosolic ribosomes", "extracellular")] <- "unknown"
plot2D(arabidposis_co3rep4, grid = FALSE, fcol = "markers")
addLegend(arabidposis_co3rep4, where = "topleft", cex = 0.7, ncol = 2, fcol = "markers")

out <- mcmc_get_outliers(arab_co3rep4_params)

nChains <- length(arab_co3rep4_params)
nChains
for (i in seq_len(nChains))
    plot(out[[i]], main = paste("Chain", i), auto.layout = FALSE, col = i)

gelman.diag(out[c(1,2)], transform = FALSE)

arab_co3rep4_params_pooled <- mcmc_pool_chains(arab_co3rep4_params[c(1,2)])
arab_co3rep4_params_pooled

arab_co3rep4_params_pooled <- tagmMcmcProcess(arab_co3rep4_params_pooled)
arab_co3rep4_params_pooled

arabidposis_co3rep4 <- tagmPredict(object = arabidposis_co3rep4,
                           params = arab_co3rep4_params_pooled,
                           probJoint = TRUE,
                           fcol = "markers")
head(fData(arabidposis_co3rep4))

par(mfrow = c(1, 2))
plot2D(arabidposis_co3rep4, fcol = "tagm.mcmc.allocation",
        cex = fData(arabidposis_co3rep4)$tagm.mcmc.probability,
        main = "TAGM MCMC allocations")
addLegend(arabidposis_co3rep4, fcol = "markers",
           where = "topleft", ncol = 2, cex = 0.6)

plot2D(arabidposis_co3rep4, fcol = "tagm.mcmc.allocation",
        cex = fData(arabidposis_co3rep4)$tagm.mcmc.mean.shannon,
        main = "Visualising global uncertainty")
addLegend(arabidposis_co3rep4, fcol = "markers",
           where = "topleft", ncol = 2, cex = 0.6)

save( arab_co3rep4_params_pooled, file = " arab_co3rep4_params_pooled.rda")
save(arabidposis_co3rep4, file = "arabidposis_co3rep4_tagm.rda")

Co3 combined

load("C:/Users/OllyC/Desktop/2019-LOPIT-Projects/2020-Harriet-Arabidopsis-/RawData/arabidposis_co3.rda")

There were 50 or more warnings (use warnings() to see the first 50)

arab_co3_params <- readRDS(file = "C:/Users/OllyC/Desktop/2019-LOPIT-Projects/2020-Harriet-Arabidopsis-/RawData/mcmc_arabidposis_co3.rds")

#intial visualisation

fData(arabidposis_co3)$markers[fData(arabidposis_co3)$markers %in% c("cytosolic ribosomes", "extracellular")] <- "unknown"
plot2D(arabidposis_co3, grid = FALSE, fcol = "markers")
addLegend(arabidposis_co3, where = "topleft", cex = 0.7, ncol = 2, fcol = "markers")

out <- mcmc_get_outliers(arab_co3_params)

nChains <- length(arab_co3_params)
nChains

[1] 4

for (i in seq_len(nChains))
    plot(out[[i]], main = paste("Chain", i), auto.layout = FALSE, col = i)

NA

gelman.diag(out[c(1,2,3,4)], transform = FALSE)

Potential scale reduction factors:

     Point est. Upper C.I.
[1,]          1          1

arab_co3_params_pooled <- mcmc_pool_chains(arab_co3_params[c(1,2,3,4)])
arab_co3_params_pooled

Object of class "MCMCParams"
Method: TAGM.MCMC 
Number of chains: 1 

arab_co3_params_pooled <- tagmMcmcProcess(arab_co3_params_pooled)
arab_co3_params_pooled

Object of class "MCMCParams"
Method: TAGM.MCMC 
Number of chains: 1 
Summary available

arabidposis_co3 <- tagmPredict(object = arabidposis_co3,
                           params = arab_co3_params_pooled,
                           probJoint = TRUE,
                           fcol = "markers")
head(fData(arabidposis_co3))

NA

par(mfrow = c(1, 2))
plot2D(arabidposis_co3, fcol = "tagm.mcmc.allocation",
        cex = fData(arabidposis_co3)$tagm.mcmc.probability,
        main = "TAGM MCMC allocations")
addLegend(arabidposis_co3, fcol = "markers",
           where = "topleft", ncol = 2, cex = 0.6)

plot2D(arabidposis_co3, fcol = "tagm.mcmc.allocation",
        cex = fData(arabidposis_co3)$tagm.mcmc.mean.shannon,
        main = "Visualising global uncertainty")
addLegend(arabidposis_co3, fcol = "markers",
           where = "topleft", ncol = 2, cex = 0.6)

save(arab_co3_params_pooled, file = "arab_co3_params_pooled.rda")
save(arabidposis_co3, file = "arabidposis_co3_tagm.rda")

#co3 rep4 markers 2

load("C:/Users/OllyC/Desktop/2019-LOPIT-Projects/2020-Harriet-Arabidopsis-/RawData/arabidposis_co3rep4.rda")
arab_co3rep4_markers2_params <- readRDS(file = "C:/Users/OllyC/Desktop/2019-LOPIT-Projects/2020-Harriet-Arabidopsis-/RawData/mcmc_arabidposis_co3rep4_markers2.rds")

#intial visualisation

fData(arabidposis_co3rep4)$markers2[fData(arabidposis_co3rep4)$markers2 %in% c("cytosolic ribosomes", "extracellular")] <- "unknown"
plot2D(arabidposis_co3rep4, grid = FALSE, fcol = "markers")
addLegend(arabidposis_co3, where = "topleft", cex = 0.7, ncol = 2, fcol = "markers2")

out <- mcmc_get_outliers(arab_co3rep4_markers2_params)

nChains <- length(arab_co3rep4_markers2_params)
nChains

[1] 4

for (i in seq_len(nChains))
    plot(out[[i]], main = paste("Chain", i), auto.layout = FALSE, col = i)

NA

gelman.diag(out[c(1,2,3,4)], transform = FALSE)

Potential scale reduction factors:

     Point est. Upper C.I.
[1,]       1.01       1.02

arab_co3rep4_markers2_params_pooled <- mcmc_pool_chains(arab_co3rep4_markers2_params[c(1,2,3,4)])
arab_co3rep4_markers2_params_pooled

Object of class "MCMCParams"
Method: TAGM.MCMC 
Number of chains: 1 

arab_co3rep4_markers2_params_pooled  <- tagmMcmcProcess(arab_co3rep4_markers2_params_pooled )

arabidposis_co3rep4 <- tagmPredict(object = arabidposis_co3rep4,
                           params = arab_co3rep4_markers2_params_pooled,
                           probJoint = TRUE,
                           fcol = "markers2")
head(fData(arabidposis_co3rep4))

NA

par(mfrow = c(1, 2))
plot2D(arabidposis_co3rep4, fcol = "tagm.mcmc.allocation",
        cex = fData(arabidposis_co3rep4)$tagm.mcmc.probability,
        main = "TAGM MCMC allocations")
addLegend(arabidposis_co3, fcol = "markers2",
           where = "topleft", ncol = 2, cex = 0.6)

plot2D(arabidposis_co3rep4, fcol = "tagm.mcmc.allocation",
        cex = fData(arabidposis_co3rep4)$tagm.mcmc.mean.shannon,
        main = "Visualising global uncertainty")
addLegend(arabidposis_co3rep4, fcol = "markers2",
           where = "topleft", ncol = 2, cex = 0.6)

save(arab_co3rep4_markers2_params_pooled, file = "arab_co3rep4_markers2_params_pooled.rda")
save(arabidposis_co3rep4, file = "arabidposis_co3rep_markers2_tagm.rda")

co3 combined markers 2

load("C:/Users/OllyC/Desktop/2019-LOPIT-Projects/2020-Harriet-Arabidopsis-/RawData/arabidposis_co3.rda")
arab_co3_markers2_params <- readRDS(file = "C:/Users/OllyC/Desktop/2019-LOPIT-Projects/2020-Harriet-Arabidopsis-/RawData/mcmc_arabidposis_co3_markers2.rds")

#intial visualisation

fData(arabidposis_co3)$markers2[fData(arabidposis_co3)$markers2 %in% c("cytosolic ribosomes", "extracellular")] <- "unknown"
plot2D(arabidposis_co3, grid = FALSE, fcol = "markers2")
addLegend(arabidposis_co3, where = "topleft", cex = 0.7, ncol = 2, fcol = "markers2")

out <- mcmc_get_outliers(arab_co3_markers2_params)

nChains <- length(arab_co3_markers2_params)
nChains

[1] 4

for (i in seq_len(nChains))
    plot(out[[i]], main = paste("Chain", i), auto.layout = FALSE, col = i)

NA

gelman.diag(out[c(1,2,3,4)], transform = FALSE)

Potential scale reduction factors:

     Point est. Upper C.I.
[1,]       1.01       1.02

arab_co3_markers2_params_pooled <- mcmc_pool_chains(arab_co3_markers2_params[c(1,2,3,4)])

There were 50 or more warnings (use warnings() to see the first 50)

arab_co3_markers2_params_pooled

Object of class "MCMCParams"
Method: TAGM.MCMC 
Number of chains: 1 

arab_co3_markers2_params_pooled <- tagmMcmcProcess(arab_co3_markers2_params_pooled)
arab_co3_markers2_params_pooled

Object of class "MCMCParams"
Method: TAGM.MCMC 
Number of chains: 1 
Summary available

arabidposis_co3 <- tagmPredict(object = arabidposis_co3,
                           params = arab_co3_markers2_params_pooled,
                           probJoint = TRUE,
                           fcol = "markers2")
head(fData(arabidposis_co3))

NA

par(mfrow = c(1, 2))
plot2D(arabidposis_co3, fcol = "tagm.mcmc.allocation",
        cex = fData(arabidposis_co3)$tagm.mcmc.probability,
        main = "TAGM MCMC allocations")
addLegend(arabidposis_co3, fcol = "markers2",
           where = "topleft", ncol = 2, cex = 0.6)

plot2D(arabidposis_co3, fcol = "tagm.mcmc.allocation",
        cex = fData(arabidposis_co3)$tagm.mcmc.mean.shannon,
        main = "Visualising global uncertainty")
addLegend(arabidposis_co3, fcol = "markers2",
           where = "topleft", ncol = 2, cex = 0.6)

save(arab_co3_markers2_params_pooled, file = "arab_co3_markers2_params_pooled.rda")
save(arabidposis_co3, file = "arabidposis_co3_markers2_tagm.rda")